<h1>Probabilistic Machine Learning: An Introduction</h1>
by <a href="https://www.cs.ubc.ca/~murphyk/">Kevin Patrick Murphy</a>.
<br>
MIT Press, 2021.


<h2>Key links</h2>

<ul>

  <li> <a href="pml1/pml1-toc-short-2020-12-26.pdf">Short table of
      contents</a>

  <li> <a href="pml1/pml1-toc-long-2020-12-26.pdf">Long table of
      contents</a>

  <li> <a href="pml1/pml1-preface-2020-12-26.pdf">Preface</a>

  <li> <a href="
    https://github.com/probml/pml-book/releases/latest/download/pml1.pdf
    ">Draft pdf file</a>,  CC-BY-NC-ND license. (Please cite the official reference below.)

    <li> <a href="https://somsubhra.com/github-release-stats/?username=probml&repository=pml-book&page=1&per_page=5">Book download statistics</a>
    
    <li> <a href="https://github.com/probml/pyprobml">Python code</a>
      
  <li> <a href="https://github.com/probml/pml-book/issues">Issue tracker</a>. Use this to report problems with the book and/or code. Be sure to specify which 
    release (date stamp) of the book you are using. Please specify pdf and print page number (which sometimes differ). For reporting small typos, please collect
    a batch of errors into a doc, and create a single issue (or add to an existing open issue list).
</ul>

If you use this book, please be sure to cite
<pre><code>
 @book{pml1Book,
 author = "Kevin P. Murphy",
 title = "Probabilistic Machine Learning: An introduction",
 publisher = "MIT Press",
 year = 2021,
 url = "http://mlbayes.ai"
}
</code></pre>

<p>
<h2> Endorsements</h2>

<ul>

  <li> "My favorite machine learning book just received a face-lift!
    'Probabilistic Machine Learning: An Introduction' is the most
    comprehensive and accessible book on modern machine learning by a
    large margin.  
    It now also covers the latest developments in deep learning and
    causal discovery. With this upgrade it will remain the reference
    book for our field that every respected researcher needs to have
    on their desk."  -- <a href="https://staff.fnwi.uva.nl/m.welling/">Max Welling</a>,
    U. Amsterdam 

    <li> "There are many books on machine learning out there, but none gives
    such a well-rounded, up-to-date, and comprehensive view of the
    field as this one. We use this book as reference reading for our
    students taking the advanced machine learning course at Oxford to
    introduce them to fundamental as well as current topics in the
    field. I'm amazed at the amount of work that went into this
    book---which will surely be used by many to train the next
      generation of machine learning experts."
      -- <a href="http://www.cs.ox.ac.uk/people/yarin.gal/website/">Yarin    Gal</a>, U. Oxford
    

    </ul>

<h2>Table of contents</h2>

<br>
    <img SRC="pml1/pml1-toc-short-2col-2020-12-27.png"
     alt="TOC 2020-12-26"
     style="height:400;">
<br><br><br><br>

<h2><a id="ack"></a>Acknowledgements</h2>

I would like to thank the following people for helping with this book.

<ul>

<li> People who helped write some sections:
  Krzysztof Choromanski,
  Justin Gilmer,
  Zico Kolter,
  Frederick Kunster,
  Lihong Li,
  Si Yi Meng,
  Aaron Mishkin,
  Byran Perozzi,
  Colin Raffel,
  Mark Schmidt,
  Sharan Vaswani,
  Andrew Wilson.
  
      <li> Proof reader: John Fearns.

      <li> People who have provided feedback on parts of the book:
Sebastien Bratieres,
	Kai Brodersen,
	Peter Cerno,
Daniel Galvez,
Abhishek Kumar,
Max Lepikhin,
Aaron Michelony,
	      Horst Stühler.
Hal Varian.

      <li> People who have helped with the code:
	Andrew Carr,
	Aurelien Geron,
	Osvaldo Martin,
	Duane Rich,
	Mahmoud Soliman,
	Theodore Vasiloudis,
	Oscar Wahltinez.

      <li> People who have helped with the figures:
	Sandeep Choudhary, and others who are credited in the figure captions.
    </ul>
    

